{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Возьмите датасет с цветками iris’а (функция load_iris из библиотеки sklearn)\n",
    "1. Оставьте два признака - sepal_length и sepal_width и целевую переменную - variety\n",
    "1. Разделите данные на выборку для обучения и тестирования\n",
    "1. Постройте модель LDA\n",
    "1. Визуализируйте предсказания для тестовой выборки и центры классов\n",
    "1. Отбросьте целевую переменную и оставьте только два признака - sepal_length и sepal_width\n",
    "1. Подберите оптимальное число кластеров для алгоритма kmeans и визуализируйте полученную кластеризацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем датафрейм, отвечающий условиям домашнего задания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  variety\n",
       "0                5.1               3.5        0\n",
       "1                4.9               3.0        0\n",
       "2                4.7               3.2        0\n",
       "3                4.6               3.1        0\n",
       "4                5.0               3.6        0\n",
       "5                5.4               3.9        0\n",
       "6                4.6               3.4        0\n",
       "7                5.0               3.4        0\n",
       "8                4.4               2.9        0\n",
       "9                4.9               3.1        0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_dict = load_iris()\n",
    "df_iris = pd.DataFrame(iris_data_dict.data[:, 0:2], columns=iris_data_dict.feature_names[:2])\n",
    "df_iris['variety'] = iris_data_dict.target\n",
    "df_iris[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)     variety\n",
       "count         150.000000        150.000000  150.000000\n",
       "mean            5.843333          3.057333    1.000000\n",
       "std             0.828066          0.435866    0.819232\n",
       "min             4.300000          2.000000    0.000000\n",
       "25%             5.100000          2.800000    0.000000\n",
       "50%             5.800000          3.000000    1.000000\n",
       "75%             6.400000          3.300000    2.000000\n",
       "max             7.900000          4.400000    2.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы использовать дискриминантный анализ необходимо обеспечить следующие условия для выборки:\n",
    "\n",
    "- нормальное распределение\n",
    "- похожесть дисперсии\n",
    "- низкая корреляция признаков\n",
    "\n",
    "Проверим наши данные на соответствие этим условиям.\n",
    "\n",
    "Стандартное отклонения ширины 0.44, а длины 0.83. Можно считать различие в СКО несущественным.\n",
    "\n",
    "Посмотрим является ли распределение нормальным."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_iris['sepal length (cm)'])\n",
    "plt.title('Гистрограмма распределения значений длины лепестков')\n",
    "plt.show()\n",
    "plt.title('Гистрограмма распределения значений ширины лепестков')\n",
    "sns.histplot(df_iris['sepal width (cm)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно ширина лепестков имеет распределение, близкое к нормальному. Но длина лепестков от нормального отличается уже больше. Поэтому дискриминантный анализ может давать небольшие ошибки. При этом выбросов не наблюдается.\n",
    "\n",
    "Данные можно считать непрерывными.\n",
    "\n",
    "Проверим значения коэффициентов корреляции Пирсона и Кенделла."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.11757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>-0.11757</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)  sepal width (cm)\n",
       "sepal length (cm)            1.00000          -0.11757\n",
       "sepal width (cm)            -0.11757           1.00000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris[['sepal length (cm)','sepal width (cm)']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>-0.076997</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)  sepal width (cm)\n",
       "sepal length (cm)           1.000000         -0.076997\n",
       "sepal width (cm)           -0.076997          1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris[['sepal length (cm)','sepal width (cm)']].corr(method='kendall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим, мы получили крайне низкие значения корреляции. В связи с этим можно сделать вывод, что метод дискриминантного анализа применить можно, но из-за небольших отклонений от нормального распределения у нас возможны небольшие ошибки.\n",
    "\n",
    "Разделим выборки на тестовые и тренировочные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_iris[['sepal length (cm)', 'sepal width (cm)']],\n",
    "                                                                      df_iris['variety'],\n",
    "                                                                      test_size = 0.3,\n",
    "                                                                      random_state = 72)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8222222222222222\n"
     ]
    }
   ],
   "source": [
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train, y_train)\n",
    "predict = lda.predict(X_test)\n",
    "print(f'Accuracy Score: {accuracy_score(y_test, predict)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7c/R5h5CZlZHq0IwxSWbJxSTV8COGMPyIIakOwxjTyqxYzBhjTIuz5GKMMabFWXIxxhjT4iy5GGOMaXGWXIwxxrQ4Sy7GGGNanCUXY4wxLc6SizHGmBZnycUYY0yLs+RijDGmxVlyMcYY0+KsbzHT7gQDQb56+1sWFv1EzwHdOfj00eTk56Q6LGO2K0lPLiLiBYqAlap6XMQ8Ae4DjgEqgfNU9Vt33lh3nheYqKp3JjtW0/ZVlFby+wNvZs2SdVSVV5Odl8V/rv0v/5x2O/137ZPq8IzZbrRGsdgVwPwY88YBg93HBOBfsCUhPeTO3x04U0R2T36opq37720vs2LhKqrKqwGorqihbGM5fxt/f4ojM2b7ktTkIiJ9gWOBiTEWORF4Wh3TgU4i0gsYCSxS1cWqWgu84C5rTKM+ev5z/DX1R8BUhcVzllG6sSxFURmz/Un2lcu9wDVAKMb8PsDysOcr3GmxpjcgIhNEpEhEioqLi7c9YtOmeTwSfYYqTimsMaY1JC25iMhxwDpVndHYYlGmaSPTG05UfVRVR6jqiMLCwmZEatqTI8YfRGZ2/ZEuxSMM3mdHCjrnpygqY7Y/ybxyGQOcICJLcYq1DhORZyKWWQH0C3veF1jVyHRjGnXWjacwcMgO5ORn4/V5yCnIplNhB6777+9SHZox2xVRjXpB0LI7ETkEuDpKbbFjgctwaovtB9yvqiNFxAcsBA4HVgLfAGep6rzG9jNixAgtKipKwhGYtiQUCjHzw7ksnLGYngMK2f8XI8nMymh6RWO2QyIyQ1VHtPR2W72di4hcAqCqjwBv4ySWRThVkX/tzguIyGXAVJyqyI83lViMqePxeBh+xBCGHzEk1aEYs91qlSuX1mJXLsYYk5h2c+Vi2ofVS9by1r/fZ/XitQw9bC+OGH8QOXnZqQ7LGJMmLLmYhH334RxuOeFvBPxBAv4AX739HS/d/ToPfXMnHboUpDo8Y0wasI4rTUJUlbvOfZDqyhoCfqexYk1lDRtWbuSFO19LcXTGmHRhycUkZM2SdZRtKm8w3V8bYNqr01MQkTEmHVlyMQnJys0kFIxeCSTb7rkYY1yWXExCuvTszOB9BuHx1j91snKzOPHSsSmKyhiTbiy5mITd/OLv6TWoBzn52eQUZJOZncFBp43imIsOT3Voxpg0YbXFTMK69enKEwvuY+5nCyhesYFdR+5E7x17pjosY0waseRimkVE2OvA3VIdhjEmTVmxmDHGmBZnVy7GGNMGqSrUfIhWPgOhMsgeh+SeiXhyUx0aEEdyEREPsDfQG6gC5qnq2mQHZowxJjYt+wdUPo3ztQyUL0SrJ0HXVxFJfbOAmMlFRHYErgWOAH4EioFsYGcRqQT+DTylqrFGmTTGGJMEGlwHlU8AtWFTqyGwAqrehNzTUhXaFo3dc7kdeAbYUVWPVtVzVPVUVR0CnAB0BMa3RpDGGGPC+L8DyYwyowqt+ajVw4km5pWLqp7ZyLx1wL1JicgYY0zjPF2IPvK7Bzw9WjuaqJpVW0xErFGDMcakSsY+IB0BiZiRieTGvC5oVc2tivxYUwuISLaIfC0is0Rknoj8KcoyfxCRme5jrogERaSLO2+piMxx59kIYMYY4xLxIF2eAu9AIAckHyQPOv4Vydg51eEBzayKrKrHxrFYDXCYqpaLSAbwmYi8o6pbus5V1buBuwFE5Hjg96q6MWwbh6rq+ubEaIwx7Zn4doBu70BgEWg5ZOyBRL0PkxrxVEXuH226qi5rbD11xk+u65s9w300NqbymcDzTcVjjDHGISKQMTjVYUQVz5XLUpyqyDXuc8FJEkOaWlFEvMAMYCfgIVX9KsZyucBY4LKwyQq8JyIK/FtVH42x7gRgAkD//lHzoDHGmFYWzz2XS4HFwMfAr1R1L7c6cpNUNaiqQ4G+wEgR2TPGoscDn0cUiY1R1eHAOOBSETkoxj4eVdURqjqisLAwnrCMMcYkWZPJRVX/parjcBpNXiEibyW6E1UtwUlOsQb8OIOIIjFVXeX+XQdMBkYmul9jjDGp0WRyEcdY4EYgH3gong2LSKGIdHL/z8Fp6b8gynIdgYOB18Om5YlIQd3/wFHA3Hj2a4wxJvXiueeyDFgB/BdYA2SLyMmqOqmJ9XoBT7n3XTzAS6o6RUQuAVDVR9zlTgLeU9WKsHV7AJNFpC7G51T13XgPyhhjTGqJU6mrkQVEnqRhLS9V1fOTFVRzjRgxQouKrEmMMcbES0RmqOqIlt5uk1cuqnpeS+/UGGNM+xbPPZen6u6duM87i8jjyQ3LGGNMWxZPVeQhbm0vAFR1EzAseSEZY4xp6+JJLh4R6Vz3xO37y0awNMYYE1M8SeIe4AsReQXnxv7pwB1JjcoYY0ybFk8jyqeBU4C1OKNRnqyq/012YK1hyZyfuerQPzI28wx+0eVc/nPdM/hr/akOC4Dykgr+fsHDHJd/DsfknMkfT7qL4hUbUh2WMcbEJWZVZBHJV9XyqDMTWKY1JVIVed3y9Vy05/9RWVa1ZVpWTiYjjx3OLS9dlawQ4xIKhfjNPtewfP5K/LUBADxeD526d+TJhfeTk5f68bGNMe1DsqoiN3bl8rqI3CMiB7mt5OsCGSQiF4jIVGJ355L2Jt33FrXV9a9Saqpq+WrKDNb+XJyiqByzPp7H6p/WbkksAKFgiMrSSj5+4fMURmaMMfGJmVxU9XDgA+BiYJ6IlIrIBuAZoCdwrqq+0jphtryFRT8R8AcaTM/IymDZ/BUpiGirn79fQTAQbDC9uqKGn2Ytbf2AjDEmQY3e0FfVt4G3WymWVrXT0IHM/3IhAX/9L3F/jZ++u/ROUVSO/rv2wZvhhYgrq+y8LAbuacMKGGPSX3OHOW7zTr7yWDKyMupNy8zOYJ+j9qbXwB4pisox9LA96d6vG74M75ZpHo+QnZfNYWcdkMLIjDEmPtttcuk5oDv/+OQ2dhs1GBEhOy+LYyYcyU0v/D7VoeHxePjHp7dx0Gmj8WX68Hg97HPUUB6Y/hdy8nNSHZ4xxjSpyY4r25Lmdlypqrg9MKeduvcnXeMzxrRtqagtFr7zju7fUSJynIi0qxb66fzFLSJpHZ8xxkTTZJIQkeeAQ0RkCrATUAmcgzN6pEmyitJKQsEQBZ3zUx2KMcbELZ4rkBHAIGA50ENVQyLS5KiQIpINfApkuft5RVX/GLHMITgjUC5xJ01S1dvceWOB+wAvMFFV74zriNqJ4hUb+NuvHmDe5wtAhB1278u1T13GwL12SHVoxhjTpHiKxcpVtRpYrqohd1ptHOvVAIep6t7AUGCsiIyKstw0VR3qPuoSixdnOOVxwO7AmSKyexz7bBeCgSBXHngTc6bNJ+APEqgN8NPMpfzfwX+kbFPadIhgjDExxZNc9haRUmCI25CyDNirqZXUUfdNmOE+4q09MBJYpKqLVbUWeAE4Mc5127yiqTMp21hOKBiqN91f6+eDZ6elKCpjjIlfPB1XelW1g6r63L8FqprR1HrgXIGIyExgHfC+qn4VZbHRIjJLRN4RkT3caX1wiuHqrHCnRdvHBBEpEpGi4uLUdtvSUlYvWUfQ37CFfk1lLSsWrkpBRMYYk5h4a4udICJ/dx/HxbtxVQ2q6lCgLzBSRPaMWORbYAe36OwB4LW6XUbbXIx9PKqqI1R1RGFhYbyhpbWd9xmEx+u8NT7devWSnZ/NbvvtnKqwjDEmbvEMc3wncAXwvfu4wp0WN3cky4+J6OhSVUvris7crmYyRKQbzpVKv7BF+wLbzU/23UbtzOB9dqRbVpCXeJOOWoMv00eXnp048NRot62MMSa9xHPlcgxwpKo+rqqP4ySIY5paSUQKRaST+38OcASwIGKZnuI24hCRkW48G4BvgMEiMlBEMnGqPb8R/2G1bSLCne/eyGWHd6UAP4cXbOKYi47ggel/ITMrrhJJY4xJqXgbQ3YCNrr/d4xznV7AU27NLw/wkqpOEZFLAFT1EeBU4DciEgCqgDPUaZIeEJHLgKk4VZEfV9V5ce63XcjMzmTMRueQf7NXCB64IMURGWNM/Jrs/kVEzgTuBD7CuRdyEHC9qr6Q/PAS09zuX9LSpk3QsyfU1kJmJqxdC506pToqY0w7k7LuX1T1eWAUMMl9jE7HxNJc/lo/C2f8xOrFa1tke0vmLuO9pz5i7c/rEltxwgTo0gU6d3Ye/ftDhlsElpEB/fptndelC1x8ccKxVVfW8EPRT6xbvj7hdaNZv3IDPxT9RFVFdYtsb3sRCIWYt24tizdtpKkfd8a0VfF0/7KXqs7BvechIlkicqeqXpf06JLsg+emcf9v/wNAwB9k4J79uHXyNXTr3SXhbVWUVnLRkKsoXrb1i3vnEYN4YPpf8XjiuLV19dXw6aewZIlztVJv4xVb/8/MhIED4arEhmKe/MDbPH7Dc3i8HgK1AXbffxduefmqZnUrU1FayR1n3susj+biy/QRDAQZf8tp/PKaXyS8re3Nx0uXcNV771AbDBLSEH0KOvDv43/BwE6dUx2aMS0qnhv6T4vIQQAicihQBJQkNapW8EPRT/xzwiNUllZRWVpFbVUtP367hOvH3tGsX5NXjLmxXmIBWFi0mDvO/Gd8G9h5Z5g1Cy66CHJzoy+Tk+Nc4cye7Swfp6/f+Y7Hrn+O6ooa51ir/cz9bAG3/zLO2CLcde6DzPxwDrXVfipLq6iprOWZ215h2qRozZhMnWWbS7j07TfYVF1Fhb+WqkCAnzZt5KxXXyQQCjW9AWPakHiSyzjgbyIyGbgJOLk99PM1+b63qI0Y6TEUDLFmyVoWz/45oW1VV1bz87zoQyN/Nunr+DeUlQUPPgjXXQf5EVcU+flw/fXwwAPO1UsCXv7769RU1tSbFqgNMOez+axftTHGWtGVbizjm3dn4q+pP0R0dWUNL971Woy1DMCL8+Y0SCIKlNf6+XL5stQEZUySxHPPZQ1wFFAAvK6qPyY9qlZQvGIDGmp4heLxedm4JrELs7KNFTHnRXbhEpcvvoByt+ccn1tyWV4O06cnvi1gw+rox+PL8LG5uDShbZVuKMfri37abErwddverC4rwx/lCkVRiitjn0PGtEXxNKIsA1YCo4F7RaTM7WusTdvvmOFk5jS8AgjU+Nll3x0T2lbX3p1jfuF26FqQWGCVlfDRR87/OTlw8snOX4APPoCqqsS2B+xz5JB6QyaH67dr1F51Yuo1sDu+zIa36jxeD8OPaLLLue3aQTsMIDejYTulYCjEiN6JvQ/GpLt4rlwK3D7F8lTVU/e8NYJLpmMnHEHn7h3JyNr6RZmdl8UZ151Ehy6JJQSPx8P4W0+POu/3jyZYq2vqVKipgV694JNP4MUXnb+9ejnTp05NbHvAGdedRF7HvHoJJis3i4v/Pj7hRplen5dL7z+frNytidmX4SWvQy7jbzkt4di2J+N22pn+HTqS5d16zuX4Mjh5t93p39GqmZv2JZ52LlNUNe7+xFIp0XYuZZvKefXet/jita/p0LWAk684lv1P3LfZ+//fM58w8bpnKd1QRvf+hVz+8IUMP3xIYhu57z6nWGziRCgIS3Klpc7N/v33hyuuSDi2Das38fLf32DGe7Po1q8rp111AsMPb/6VxuxPv+elu19n7dJihh66J6dfcyKFfbs2e3vbi0q/n6dnfcebCxeQk5HBOXsN5cRddrXRRk3KJKudSzzJ5VtVHd7SO06GdtWI0hhjWkGykks83b8MEpEG/Xqp6gktHYwxxpj2IZ7kUgzck+xATPNVbK5g1iffk52bxZCDd8eXEW+XccY0j2oV1EwH8UDmKESyUh2SSTPxfAuVq+onSY/ENMs7j33Ag5c/7tysV/BlernjrRvYdeTgVIdm2qlQ1ftQejVOn7KuTvcjWQekLCaTfuJpRPnXpEdhmmXJ3GU8dPnj1FbVOj0NlFVRuqGc68feQW2Nv+kNGJMgDa6BzVeBVoGWb3nopkvRkLVzMlvFk1y2jMsCICKdReS3SYzJxOmdxz7AXxtoMD0UClE0dWYKIjLtXvVbQJSGwQJUJ15N3rRf8SSXi9yRJAFQ1U3ARckLycSrvKQiag8AGlIqSxNvbGlMUzRUAUS5KtaAcxVjjCue5OKRsOrQl08AACAASURBVEr47uBfiXVuZZJizIkjyc7PbjA94A8ybBvasBgTi2QdCDQ858ALmQe2djgmjcWTXKYCL4nI4SJyGPA88G5TK4lItoh8LSKzRGSeiPwpyjJni8hs9/GFiOwdNm+piMwRkZkiYo1Xohh1/D7sPnpnsvOcmjoiTsv7s288ma69rAt3kwQZQyH7SJCwnrslB3JORjLi76nbtH/xNKL0ABcDh+OUrL4HTFTVYBPrCZCnquUikgF8BlyhqtPDltkfmK+qm0RkHHCrqu7nzlsKjFDVuEe22h4bUQYDQaa9Op2PX/yCnIJsjrnwCPY6cLdUh2XaMVWFmo/QqtdBPEjOyZB5gPUy0EalrIW+u/McoL+q/tCsnYjk4iSX36hq1EE/RKQzMFdV+7jPl2LJxRhjkiplwxyLyAnATNyiMBEZGq3Ffox1vSIyE1gHvB8rsbguAN4Je67AeyIyQ0QmNLKPCSJSJCJFxcXF8YRljDEmyeJpRPlHYCTwMYCqzhSRAfFs3C06G+pWZZ4sInuq6tzI5dwRLi8AwlthjVHVVSLSHXhfRBao6qdR9vEo8Cg4Vy7xxGWMSR+qCv4i8M8Db2/IOhSnJD3J+w1thOr3gQBkHYJ4bdiDlhRPcgmo6uZtKU9V1RIR+RgYC9RLLiIyBJgIjFPVDWHrrHL/rnNHwRwJNEguxpi2S7Ua3fhrCMwH9YNkguRD1xeS+mUfqnobNl+LU3ijwJ1o/hV48i9M2j63N/HUFpsrImcBXhEZLCIPAF80tZKIbGl86d6zOQJYELFMf2ASMF5VF4ZNzxORgrr/cUbCbHDFY4xp27T8EfDPBa0E/KAVECpGS65O3j5DG93EUgNUAdXO/+X3o/6Fja9s4hZPcvkdsAfOO/E8UApcGcd6vYCPRGQ28A3OPZcpInKJiFziLnML0BV4OKLKcQ/gMxGZBXwNvKWqTVZ/Nsa0MVWTcL5awoXAPxsNJWnA2+qPQKKNzOpHq6ckZ5/boSaLxVS1ErjRfcRNVWcDw6JMfyTs/wuBBtehqroY2DtyujGmvWmsRUOjrR22QQCi1pINOUVzpkU0mVxE5MNo01X1sJYPxxizXck+BiqfB2rDJgr4dkI8SWoInHUIcHu0YJDsscnZ53Yonhv6vYGzkx2IMWb7I/m/Q2umQXANUOm09icD6Xh38vbp7YEW/AHK7gYCODf0MyH3dCTTCkxaSjzJpUpVZyQ9EmPMdkc8HaDbm1DzP7R2FuLrD9nHI56CpO7Xk/crNOsAtGoKEECyj0QyrD++lhRPcrG2I8aYpBHJgOxxSPa41t2vbxBScHmr7nN7Ek9y2VtESnH6FdO6v6raIamRGWOMabPiqS0Wrc6eiaK8pIIPnv2U5QtWsvOInTj49NFk5TQcW3zBN4t44qbnKVm3mf1P2JczbzyZzMzkt0g27Y+GKqH6LdT/Pfh2RnKORzz5yd+vfzZa9a7TcWX2sUhG8ztLDYXKoeweqP0GvP2hw7V4fDu0YLQmFeLpFVlwbugPVNU/i0g/oJeqft0aASYilR1XLluwkivG3Ii/JkBNZQ3Z+dl06JrPQ1/fSafCjluWe/Yvr/LkTS/UWze3Qw7Pr/g3ufk5rR22acM0uAbdcCqEyoFKIAckB+n6MuLrl7T9hkrvgspn2do+JRPyL8GTn/gAtaHAclh/NM6N9TAd78OT07rFZNurlHVcCTwMjAbOcp+XAw+1dCBt3d/Pf5iKkgpqKp0PXHV5NRtWbeKx65/dskx1ZTVP3vxCg3UrS6u4d8K/Wy1W0z5o6e0QWo+TWACqQEvQ0j8mb5/++VD5jLMvQu6jGsr/hQaWJb7BTb+lQWIB2HzNNsVpUi+e5LKfql6K00dC3TDHNhJlmKqKahYWLWrQLivoD/LZpK0XeB+98EXM6hHT37IKeSZBNZ/QcDz7ENR+gWqUce5bgFZ/QP02KVvmQE3UJnGNC8bqbqWGUGBp4tszaSOe5OJ3hzZWcPoMo+EZvV3zej049RyizMvYessqMzv2fRWvz25tmQRF7cIEwEus83GbdykZ7vYjeaBZPRk3Fqfdh2zL4kku9wOTge4icgfOoF9/SWpUbUxmdib7HDUEr88TMT2DI3918JbnB58+Go83+kt++NkHJTVG0w5lH0vDL+AMyD46eaNCZo8j+teGQtZRiW8vI0ZRv+Tj8VkX+G1Zk8lFVZ8FrgH+CqwGfqGqLyc7sLbmqom/occOheQU5JCZnUF2XhY7DhvIuX/65ZZlfD4f1z39uwY/1nrv1IPf3nte6wZs2jwpuA58g93x7LNA8sC7A9Lh5uTt09cfCm509udWIIAs6HAH4i1MfIOdHwaJbNXghc5PbHuwJqXiqS02PNp0Vf02KRFtg1QPcxwMBvn2/dmsXryOQUP6s8eYXaP+gizdWMazt09i4+qNHHbWAYw+ft8URGvaA1WF2q8h8CP4BkLmaETiKZDYxv0Gi6HmI8AD2Ychni7N3lYoFILqV6Dmc/DtCHkT8HiyWy5Y06hk1RaLJ7mU4XSZH/4tqenYcWWqk4sxxrQ1yUou8bTQX5SOicQYY0z6iie5FIrIlThVkVcBX6nq2uSG1b4tnPETbzz0LhtWlzDq+H046txDyMmrXwygqhRNnck7j32Iv9bP4WcdyIGnjsLrTY9aZT9u2MBTs75leelm9u/bnzP3GkKHrOYVZQRCIe7+YhqvLfgeQTh9jz25cr/98XjqF+9UB/xMmv897y1eRNecXM7Za2+G9erd7GNYtrmEp2Z9x48bNjCsVy/GDxlGt9zcZm8vnWlgEVrxXwguh8xRSO4ZTqeRzVFbC5nWGsE0Lp5isT/i1D3Mwel+/wDgj6r6ZBPrZeOMeZ+Fk8ReUdU/RiwjwH3AMTgtwc6ru5cjImPdeV5goqre2dTBtIVisfee/pj7f/sfaqv9aEjJys2ke/9CHvzqr+QWbG2h/6/fP8HbEz+gusJplJmdl8XQw/bitteuSV5NoDh9vHQJl779BrXBIEFVsr0+OmZn8+aZ4xP+cg6FQhz45ERWl5fVmz6oU2f+96vztzyvDvg55aXnWVqyiapAAAGyfD5uPPAQzt4r8W7Sv1u9inMmv0IgFMQfCpHl9ZLjy+D1M86hX8eOTW+gDdGaT9BNvwP8OANwZYOnI9L1NcTbNbGNFRfDLrvADz9AYTNu4Ju0k7IW+qr6J1W9RVX/oKpnA/sA18ax7RrgMFXdGxgKjBWRURHLjAMGu48JwL8A3HY1D7nzdwfOFJHd4zymtFVTVcMDlz1GTWUtGnKSek1lLWt/LmbKI+9tWW7Fj6uZ8u/3tyQWgOqKGmZ+OIfvPpjT6nGHC6ly7f/epSoQIOj+MKkOBthYVcnD33yV8Paemzu7QWIBWFyyibd/3NrA7pXv57HETSzgNLqqDgS4Y9rHlNdGa9TXuOs/fI+qgB9/yGmyVRMMUlpbw52ff5LwttKZagjdfD1OwUPdyI7VENqIVvwr8Q2+9hps2gSvv96CUZr2KOFqJaq6XlWb7KVOHeXu0wz3EXmZdCLwtLvsdKCTiPQCRuLc61msqrXAC+6ybdqi75bi8TS86qitqmXapK1fzN/9bzZEuTqprqhJeUv+5Zs3U17bcChYfyjE/5YsSnh7UxYuiDlv8vx5W/5/Z9FCqgMNuwnJ8Hj4bs2qhPZZUVvLTxs3NZgeUmXasp8T2lbaC66AUEWUGX6obkaL+ieeqP/XmBiSWmdRRLwiMhNYB7yvqpE/bfsAy8Oer3CnxZoebR8TRKRIRIqKi4tbLvgkyO2QQygYvXODgi55W/7P65jrtvqvz5fppUOX5A6i1JS8zEyCMboWKchs2AN0UwqyYq/TMXvrvM7Z0Tv1DKnSIcH9Zni9eGIULeZmtLNW4ZJHzLHoE+09edMmmOH+uCkqgpKSbQrNtG9JTS6qGlTVoUBfYKSI7BmxSLRPuDYyPdo+HlXVEao6ojDNy4AH7NGP7v27IRFXL9l5WfzismO2PB99woioVy5er5cjxqe2JX+33FyG9+yNL+Jme44vg18PjdokqlG/H7V/zHlX7Ld13vghQ8nx1a9/IkDnnByG9OiZ0D4zvV7G7TSYzIjKEdk+H+OHDE1oW+lOvF0hYxgN6u5IDpJ7XuMrT5gAXbpA587Oo39/qEu+GRnQr9/WeV26wMUXJ+MQTBuV/NZWgKqWAB8DYyNmrQDC+wbvi1MjLdb0Nk1EuH3K9fQa1IOc/GxyOzit+X95zYmMHDdsy3I5+Tn85e0bKOiST25BDrkdcsjOy+Kapy6j54DuKTwCx/3jjmNwl67k+DLIz8wk0+vltN334JTd9kh4W7sX9uDykaMbTL/+gIPp17HTluf79e3HlfvtT5bXS35mJnkZGfQqKODJE09pVgWHPx96BEN79iLb5yM/M5Msr5cjB+3IxfuMTHhb6U463eu25M8ByQcyIedUyDmp8RWvvhq6d4fKSucqpbwcKtwitooK53lJiTO/e3e46qqkH4tpO+KpLZYNXADsAWypa6qq58dciS0dXPpVtUREcoD3gL+p6pSwZY4FLsOpLbYfcL+qjhQRH7AQOBxYidOI8yxVnRe5n3BtobYYONWM53/1I6Xry9ht1GA6doteJTTgDzBn2nwCtQH2Omh3snMTL3ZKFlXl++J1rCkvZ8/uPeiRv20DVJVWV/PMnFl4RDhnyFDyY1R13VxdTdHqlXTKzmZYz94xi7fitXDDepZv3swu3brRt0P7qiUWTlUhMB+CayBjD8TbI74Va2qcpPHEE04SiZSTAxdcAPfcY9WT26hUttB/GViAM57LbTgDh81X1SuaWG8I8BROVWIP8JKq3iYilwCo6iNuVeQHca5oKoFfq2qRu/4xwL3u+o+r6h1NHUxbSS7GtDl//jPcdZdztVInPx+uuQZuTl5fZib5UtlCfydVPU1ETlTVp0TkOWBqUyup6mxgWJTpj4T9r8ClMdZ/G3g7jviMMcn2xRdbE4vPB4GA83z69NTGZdJWXOO5uH9L3BvyHYEBSYvIGJNeKivho4+c/3Ny4OSTnb8AH3wAVVWpi82krXiSy6Mi0hm4GXgD+B64K6lRGWPSx9Spzr2XXr3gk0/gxRedv716OdOnNlmQYbZDTRaLqepE999PgEHJDccYk3aWLYPTT4eJE6HAbWe1776wYAFcdBH83M4anpoWEc8N/VuiTVfV25IS0TawG/rGGJOYlPUtBlS4j4vC/o/Wn4QxxhgDxFcsdg+AiJxT978xxhjTmERa6DdefmaMMca4mrxyEZE3cRLLIBF5o266qp6QzMCMMca0XfE0ovy7+9eKxIwxxsQlnnsu7Wv0JGOMMUkXT7FYGVvvt9T1Eqiq2swBuI1pnmWbS7jzs0/5YsUy8jMzOXfv4Zw/dDheT6t07t3o2PFVfj/3ffUlkxbMIxAKcfSOg/nD/gfQJSexYZ8TFSqfCBUPgFYBWZB/MZ78y5K6T2PiEc+n8j5gLnCmqha4D0ssplUVV1Rw4gvP8N7iRZTW1LCqrIx7p3/O9R+81/TKLRJAMfTs6fyNoKr86rVXeGrWt6yvrKSkuppJ8+dx0ovPURNl9MyWEip/GMrvchMLQA2U309os3WgYVKvyeSiqjfhDDF8tIh8ICJjkh+WMfU9OetbqgIBQmGNfqsCAd5cuIA15WXJD6CRseNnrF7F/PXF1AS3jvjoD4XYUFXJO4sWJi+m8oeiT6+yIYhN6jWZXERkOM7AXU8CDwMPi8iURlcypoXNWL2K2mDD4XozvV4WbtiQ/AAaGTv+++J1BEMNh36u9PuZuWZ1EoPyx5geJBRK3hWTMfGIp7ZYZC2xjUBetAWNSZadOndhxqqVBCO6K/KHQvTtkORS2mhjx3faOkpmv44d8Xm89a5cAHJ8PgZ17pLEwDxAw6QGgscTz0fbmOSJp7bYoc3ZsIj0A54GeuJ8Ah5V1fsilvkDzuBjdbHsBhSq6kYRWQqUAUEgkIy+b0zbcf6wfZi8YD5Vga2/1jM9Xob37N3yX+ATJsArr0BdIgsEnDHja2u3jh3vcz86Ihx86ql03n9fqgP+LclPcK6qfrHrbi0bW7jsE6D6tYbTMw9P3j6NiVM8xWJdReR+EflWRGaIyH0i0jWObQeAq1R1N2AUcKmI7B6+gKrerapDVXUocD3wiapuDFvkUHe+JZbt3KDOXXjixJMZ2KkzPo+HDI+XsTsN5pHjTmz5nSU4drzn6qt5+dQzGd2vPz6PB5/Hw149evLSqWfSISu78X1tiw53QuaR9adlHgSdHkzePo2JUzy9Ir8PfAo84046GzhEVY9IaEcirwMPqur7MeY/B3ykqv9xny8FRqjq+nj3Yb0ibx9Ka6rJ8vrI8iWx6KeZY8dX+Z2rl/xWHE8+FKqF0Grw9MLjsXHsTWJS2StyF1X9s6oucR+3A52aXCuMiAzAGfL4qxjzc4GxwKthkxV4z71ampDI/kz71iErO7mJBSArCx58EK67zhkrPlx+Plx/PTzwQIN2LzkZGa2aWAA8nkw8vh0ssZi0Ek9y+UhEzhARj/s4HXgr3h2ISD5O0rhSVUtjLHY88HlEkdgYVR0OjMMpUjsoxvYniEiRiBQVR2mDYLZf/mCQpq7MmxQ5djzY2PHGxCGeYrEynNphQZz7lB62jufSaEt9EckApgBTVfUfjSw3GXhZVZ+LMf9WoFxV/x5tfh0rFjMA7/30I7dP+5iVpaUUZGVx8fB9uXjESDwiTa8crrISunRxishycuD44+HNN50x47OynFpkdWPJG9NGpaxYzG2R71HVDFX1uf832VJfRAR4DJjfRGLpCBwMvB42LU9ECur+B47C6SXAmEZNW7aUK6e+zYrSUhQoranhwW+mc99XXya+MRs73phmi6dvseHRpqvqt02sOgYYD8wRkZnutBuA/u76j7jTTgLeU9Xw0S17AJOd/IQPeE5V320qVmP++eUXVEd0uVIVCPDYd0Vcuu9+ZHq98W/Mxo43ptniKRYLAT8CK6nfceVhSY4tYVYsZvZ59CE2VVc3mJ7l9fHJeRfQPS8/ylrGbL9SWVvsSGANMAM4RVUPTcfEYgzA4C7dok7P8HronG33R4xpLfHcc/lAVQ8GvgTeEpEb3arDxqSdq/YfQ3ZENeUcn4/L9h1FRiJFYsaYbRJPC/3/E5H/AwYAk4FfAouTHJcxzbJv7748dvxJ7FHYnQyPh94FBdx80KFcNNw6eTCmNcXTEq0g4vmrUZcyJk2M7tefN88cn+owjNmuxdNx5Z9aIxBjjDHtR8zkIiJvsnV440iqqknoMdC0RxsqK6kK+OlT0AFJtCFjhJAqK0tLyc/MpHMaNmAsrqjAHwrSK79gm4/VmLassSuXWK3hBXgxCbGYdqa4ooLL353Cd2tW4xGhc3Y2dx05ljH9dmjW9j5cspjrP5hKeW0tQVVG9enHP48+Ji2SzLLNJfzunSn8sGE9AvTKL+DesccypEfPVIdmTEo02c4l6koiy1W1XxLi2SbWziV9qCpjn32KxZs21hvgK8fn4+2zzmWHTgn1fcoPG9Zz0ovP1msgmeHxsEf3Hkw6/awWi7s5/MEgBz75H9ZXVtYbhjkvI5NPzruALjlWudKkr1S2c4lmG3sDNO3dzDWrWVlW2mDkyEAoxDNzZsZYK7bHv5uBP2KkR38oxA/ri1m4Ie5RGZLi46VLqKj110ssAEENMXnB/BRFZUxqNXbPZQ7Rk4jgdM9iTEyry8sRGt5z8IdC/FxSkvD2lm0uaZCoAHweD2vKy9m5a/TGk61hdXkZgVCwwfTqQIDlmxM/VmPag8buuRwXY7oAnychFtOODOnRI+oXbo7Px/79+ie8vdF9+zNzzeoG49TXBoPsXti92XG2hL179ora43JuRgb79u6bgoiMSb2YxWKq+nOMx1Kc7veNialvh46csMuu5IS1lvd5PHTKzuGU3fZIeHvjhwylICsLn2frKZvj83HWXnvTLTe19zT27tGTkX361esZINPrpU9BB47acacURmZM6jT3hv4yVU3852eS2Q399BJS5fm5s3l61ndU+Gs5atBgLt13P7o2Mxmsqyjn/q++5KOlS+iYlcX5w/bhlN32SIsqv/5gkCdmfsuL8+bgDwU5YedduWTEfq0+KqUxiUrWDf2YycUdJCzWPZccVU3yOLOJs+RijDGJSVZyiZkgVDWy2xdjjDEmLs2timy20cIN6/lqxXLKa2tTHUpSqSrz1q3l65UrqA74Ux2OMaaVJK1oS0T6AU8DPYEQ8Kiq3hexzCE4wxsvcSdNUtXb3HljgfsALzBRVe9MVqytaU15GRe8MZmlJZvweTz4QyGuHn0A5w/bJ9WhtbjFmzZy/huTWF9ZiUeEkCq3H3oEv9h191SHZoxJsmTeNwkAV6nqtyJSAMwQkfdV9fuI5aapar1qzyLiBR7CGahsBfCNiLwRZd0254I3JrNww/p6bTbu+fIzdunWrdndoqSjYCjEOZNfZm15eb0bdzd8+D67dCtkt26FKYvNGJN8SSsWU9XVqvqt+38ZMB/oE+fqI4FFqrpYVWuBF4A231Hmoo0bWFqyqUFjwKpAgCe++zZFUSXHN6tWUlZT06BGiD8Y5NnZibfQN8a0La1yz0VEBgDDgK+izB4tIrNE5B0RqWsA0QdYHrbMCmIkJhGZICJFIlJUXFzcglG3vJLq6nrtNMJtqKps5WiSa1N1VdQqwkFViivb17EaYxpKenIRkXycAcauVNXSiNnfAjuo6t7AA8BrdatF2VTUOtOq+qiqjlDVEYWF6V3UsnthdwKhUIPpWV4vhw8clIKIkmdErz7UBqO10M9od8dqjGkoqclFRDJwEsuzqjopcr6qlqpqufv/20CGiHTDuVIJ73W5L7AqmbG2htyMDK4/4OB6rdazvF4K8/L41d7DUhhZyyvMy+PiffYlx5exZVq2z8eATp04cZfdUhiZMaY1JLO2mACPAfNV9R8xlukJrFVVFZGROMluA1ACDBaRgcBK4Awgtf2qt5Bzhgxl567deGLmDNZVVHD4wB05Z8hQOmRlpTq0Fvf7UWMY1rM3/539HaU1NRw7eBd+ucdeZPnSrv2tMaaFJfNTPgYYD8wRkbo7uDcA/QFU9RHgVOA3IhIAqoAz1OkyICAilwFTcaoiP66q85IYa6sa2acvI/tsHx0aHjJgIIcMGJjqMIwxraxZfYulK+v+xRhjEtPq3b8Y0xJqAgGmLVtKea2f/fv1o3tefqpDMsa0AksuJmlmrlnNea+/SiikhFACoRCXjxzNb/fdL9WhGWOSzPoWM0lRGwxy/uuTKK2podxfS6XfT20wyEPfTKdo1cpUh2eMSTJLLiYppq9YHrVNT3UgwEvz5qQgImNMa7LkYpIiVg/ICu2+J2hjjCUXkySj+vbDH+XKJTcjg+N23iUFERljWpMlF5MUHbKyueXgQ8n2+fC4fYzlZmQwolcfjt5xcIqjM8Ykm9UWM0lz5p5DGNazFy/Pm8vmmmqO3nEwhw0chDdG553GmPbDkotJql27FXLzwYemOgxjTCuzn5DGGGNanF25pLGVZaW8u+hHAqEghw/ckZ26dE11SMYYExdLLmnqle/ncvNHH6AoIVXunf4lE/YZwe9HjUl1aMYY0yQrFktDxZUV3PzR/6gJBqgNBgmEQtQEA/zn2yLmrVub6vCMMaZJllzS0IdLFm+pvhuuNhBkysIfUhCRMcYkxpJLOmpHwyAYY7ZPllzS0GGDdiQUJcFk+bzWut0Y0yYkLbmISD8R+UhE5ovIPBG5IsoyZ4vIbPfxhYjsHTZvqYjMEZGZIrJdjQBWmJvHbYccTpbXS6bXi8/jIcvr44JhI9ije49Uh2eMMU1KZm2xAHCVqn4rIgXADBF5X1W/D1tmCXCwqm4SkXHAo0D4YB+Hqur6JMaYtk7bYy/2778D7/y4kEAoxBGDrCqyMabtSFpyUdXVwGr3/zIRmQ/0Ab4PW+aLsFWmA9vHwPJx6lPQgQuHt/joo8YYk3Stcs9FRAYAw4CvGlnsAuCdsOcKvCciM0RkQiPbniAiRSJSVFxc3BLhGmOM2UZJb0QpIvnAq8CVqloaY5lDcZLLAWGTx6jqKhHpDrwvIgtU9dPIdVX1UZziNEaMGGHVrEyLW1KyiRfnzqa4spKDdxjAuJ12JsPrTXVYxqS1pCYXEcnASSzPquqkGMsMASYC41R1Q910VV3l/l0nIpOBkUCD5GJMMr3/0yKumPoWgVCIQCjE1EU/8th3M3jp1DPI8lkHF8bEkszaYgI8BsxX1X/EWKY/MAkYr6oLw6bnuZUAEJE84ChgbrJiNSaa2mCQq99/h+pAYMuQzZUBPz9u3MDzc2enODpj0lsyf3qNAcYDc0RkpjvtBqA/gKo+AtwCdAUednIRAVUdAfQAJrvTfMBzqvpuEmM1poG569YSrZy1OhDgzYULOG/o8FaPyZi2Ipm1xT4DGvZhUn+ZC4ELo0xfDOzdcA1jWk+Ozxe1MSs4o2oaY2KzFvrGxLBrt0K65uQ2+IWU48vg7L2GpiQmY9oKSy7GxCAiTDz+JLrk5JKfkUluRgZZXi+n77EnR++4U6rDMyatWXUXYxoxuGtXvjh/Ap8vX8amqir27dOHvh06pjosY9KeJRdjmpDh9XLIgIGpDsOYNsWKxYwxxrQ4Sy7GGGNanCUXY4wxLc6SizHGmBZnycUYY0yLE21H47WLSDHwc8TkbkBbH3DMjiE92DGkh/ZwDJA+x7GDqha29EbbVXKJRkSK3P7K2iw7hvRgx5Ae2sMxQPs5jlisWMwYY0yLs+RijDGmxW0PyeXRVAfQAuwY0oMdQ3poD8cA7ec4omr391yMMca0vu3hysUYY0wrs+RijDGmxbWb5CIiXhH5TkSmRJl3iIhsFpGZ7uOWVMTYFBFZKiJz3BiLoswXEblfRBaJyGwRSbtxduM4hrR/zK0rlAAAB59JREFUL0Skk4i8IiILRGS+iIyOmN8W3oemjiGt3wcR2SUstpkiUioiV0Ysk9bvQ5zHkNbvw7ZoT13uXwHMBzrEmD9NVY9rxXia61BVjdWwahww2H3sB/zL/ZtuGjsGSP/34j7gXVU9VUQygdyI+W3hfWjqGCCN3wdV/QEYCs4PR2AlMDlisbR+H+I8Bkjj92FbtIsrFxHpCxwLTEx1LEl2IvC0OqYDnUSkV6qDak9EpANwEPAYgKrWqmpJxGJp/T7EeQxtyeHAT6oa2ftGWr8PEWIdQ7vVLpLL/7d3trF2VFUYfl4pUqA1QUGwpaapJFqVWMAUsWgi1iY1pEFtBBQN9AdSRRMRiZqooCZoYlKNLWqpaRMKSpSi1wSholLU1FpobSn4DU1bEYtSlX5IQ339sffE6XjmnHNxcs+ce9eT3JyZtb/WOuueWdl7ZtYGvgRcB/y7S53zJG2T9ANJrxojvUaLgfWSHpR0ZYfy6cDu0vmeLGsTvWyAdvtiFvAksDovs66SdGKlTtv90I8N0G4/lLkE+GYHedv9UKbOBhgeP4yKoQ8uki4E9tp+sEu1LaT8Oa8BvgJ8d0yUGz3zbJ9Nmu5/QNIbK+Xq0KZtz5L3sqHtvpgEnA181fZZwAHgY5U6bfdDPza03Q8A5CW9RcC3OxV3kLXJD0BPG4bCD8+FoQ8uwDxgkaSdwLeACyStLVew/U/b+/PxXcCxkk4ec017YPvx/LmXtDY7t1JlDzCjdH468PjYaNcfvWwYAl/sAfbY3pTPv0O6UFfrtNkPPW0YAj8ULAS22P5Lh7K2+6Gg1oYh8sOoGfrgYvvjtk+3PZM09fyx7cvKdSSdJkn5eC7J7r+NubJdkHSipKnFMbAA2FGpNgK8Nz8l8zrgH7b/PMaq1tKPDW33he0ngN2SXp5FbwYeqVRrtR/6saHtfihxKfXLSa32Q4laG4bID6NmPD0tdhSSrgKw/TVgMbBU0rPAIeASty81wanAnfn/bBJwm+27K3bcBbwV+ANwELhiQLrW0Y8Nw+CLDwK35uWMR4ErhswP0NuG1vtB0gnAW4D3lWRD5Yc+bGi9H54rkf4lCIIgaJyhXxYLgiAI2kcElyAIgqBxIrgEQRAEjRPBJQiCIGicCC5BEARB40RwCQaOpB2SHslZYf8k6fpB6zTekXS8pA05oWIT/d0r6aQm+grGBxFcgraw0PYcYNmgFZkgLAHW2T7SUH+3AO9vqK9gHBDBJWgDxwLPdCqo7HfxhKRrs3xnkSZD0lpJO/Lx5ZKWl9ovl3R5Pv6UpM15prSyeDO6w5hrJD2WxzxcGuejuf12STdk2UxJh/Tf/Tgek7Qml50i6Y7cZrOkeVk+RdJqpX1vtkt6h6QP5/a7JD2Zj1fl/gvbZislOJyRz6/JtuxQ3iekos+jkr5Y852/G/heyebrsj7bJH0+y+6TtLFU5wuS6l6MGyG9iR4EwDh+Qz8YKqYCT9eUHQNssL2o03KZpDOBV/c5znLbn8ntbgEuBL5fM+ZHbK9TylmHpAWkfUPmkhImjigl5dxFSqVe7NuxOPcLaU+VZbZ/JumlwD3AbOCTpFQlZ+Y2J9neByzLgfC1tq/OZTPz53RS7rx32d4t6RzSG+nnZn02SdoA7Cv0kXQq8DBwbeU7ez4wy3Zh20LgIuBc2wclvbBU/XmSXgn8lpSf7ECnL9b2PknHSXqR7XGRviT4/4iZSzBQ8pr/VNsdL1rA8cC/unTxOeDTFdnFxUwCuLgkf5OkTZIeAi4A6tKbdxpzQf7bSspk+wpSsOnGfGB51mMEeIFS7rX5wIqiUg4s3ZgC3A3cZ/vhLDsfuNP2gZz4cB3whlz2sjzm70gBrsrJQHl/l/nAatsHsz5PlcpWk4LYwqxDN/YC03rUCSYIEVyCQTOLdBGsYxr1mW5fD+wHtlXkt9uek2cTtwNImgzcBCzOM4abgcmjGFPAjUW/ts+w/Y0uekP6fZ1XajPd9tO5r9HkXZoB3EgKjrNL+tRRzKReAlxaLKOVOMTRtnfT5wHSbopLgLU1dQom576DIIJLMHDeCWzsVJBnNW8Hfl7T9nqg3z3Hi4vpXyVNISUM7DTmGcBM/jcT8j3AktwWSdMlvbjHmOuBq0t9z6mR93rK6te2byMlo/x6vld0P3CRpBOUMlC/Dfhppd0zwBHgqP7zTOmYHHALfZYoJVmksiwGaR+Sp2rS3hc2CDgN2NnDlmCCEPdcgoEhaSnwWWCXpPOz+BTShW8LaQuF3wN31HSxyfYfi/sS3bD9d0k3Aw+RLoCbO+gzjXST+0rbhyvt1+dZw8b8HMB+4DLSxbuODwErJG0n/dbuB64iLeWtyDfqjwA3kJa1etmwQdJvgKW2b8oPDvwyF6+yvTV/F8Wy2HHAD21v79DdetLS2r05c/Uc4AFJh0nZhj9RGnclsLLagaRfFfeagHOAX9h+tpcdwcQgsiIHAyPfoN9pe00/8qA5JJ0FXGP7PQ3192VgxPaPmugvGH5iWSwIJiC2twI/UUMvUQI7IrAEZWLmEgwMSZMAV1/kq5MHQTA8RHAJgiAIGieWxYIgCILGieASBEEQNE4ElyAIgqBxIrgEQRAEjfMfU55IF3t7e7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test['sepal length (cm)'],X_test['sepal width (cm)'], c=predict)\n",
    "plt.title('Предсказанные цветковые группы с центроидами групп')\n",
    "plt.xlabel('Длина лепестков (см.)')\n",
    "plt.ylabel('Ширина лепестков (см.)')\n",
    "# центроиды\n",
    "plt.scatter(lda.means_[:, 0], lda.means_[:, 1], c='r', s=150, marker='*')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(X_test['sepal length (cm)'],X_test['sepal width (cm)'], c=y_test)\n",
    "plt.title('Действительные цветковые группы')\n",
    "plt.xlabel('Длина лепестков (см.)')\n",
    "plt.ylabel('Ширина лепестков (см.)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим есть ошибки определения групп. Это связано с пересечением значений в 2 группах в исходных значениях и вероятно в для получения более точных результатов нужно добавлять дополнительные признаки в наш набор данных.\n",
    "\n",
    "Попробуем определить группы с помощью алгоритма кластеризации к-среднего. Для начала найдем оптимальное количество кластеров с помощью метода локтя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "k_inertia = []\n",
    "ks = range(1,20)\n",
    "for k in ks:\n",
    "    kmean = KMeans(n_clusters=k)\n",
    "    kmean.fit(X_train_scaled)\n",
    "    k_inertia.append(kmean.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ks, k_inertia)\n",
    "plt.plot(ks, k_inertia, 'ro')\n",
    "plt.title('График зависимости инерции от количества кластеров')\n",
    "plt.xlabel('Кол-во кластеров')\n",
    "plt.ylabel('Инерция')\n",
    "plt.xticks(ks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по полученному графику оптимальный размер кластеров - 4\n",
    "\n",
    "Теперь определим наши группы с помощью метода к-среднего и построим график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmean = KMeans(n_clusters=4)\n",
    "kmean.fit(X_train_scaled)\n",
    "clusters = kmean.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test['sepal length (cm)'],X_test['sepal width (cm)'], c=clusters)\n",
    "plt.title('Предсказанные цветковые группы с центроидами групп')\n",
    "plt.xlabel('Длина лепестков (см.)')\n",
    "plt.ylabel('Ширина лепестков (см.)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
